Rangeinet: Fast Lidar Point Cloud Temporal Interpolation

Zhao, Lili; Lin, Xuhu; Wang, Wenyi; Ma, Kai-Kuang; Chen, Jianwen

doi:10.1109/icassp43922.2022.9747825

Cited by 6 publications

(1 citation statement)

References 15 publications

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“…A few research [12,13,14] has illustrated that a U-Net architecture with skip connections is capable of frame interpolation or extrapolation. A simple approach for frame prediction is to naively set the reference frames and predicted frames as the input and output of the U-Net, respectively.…”

Section: Inter-frame Predictionmentioning

confidence: 99%

University Technology Commercialization in Taiwan: National Taiwan University (NTU) and National Taiwan University of Science and Technology (NTUST)

Liu¹,

Chen²,

Chiou³

et al. 2011

Academic Entrepreneurship in Asia

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The large amount of data collected by LiDAR sensors brings the issue of LiDAR point cloud compression (PCC). Previous works on LiDAR PCC have used range image representations and followed the predictive coding paradigm to create a basic prototype of a coding framework. However, their prediction methods give an inaccurate result due to the negligence of invalid pixels in range images and the omission of future frames in the time step. Moreover, their handcrafted design of residual coding methods could not fully exploit spatial redundancy. To remedy this, we propose a coding framework BIRD-PCC. Our prediction module is aware of the coordinates of invalid pixels in range images and takes a bidirectional scheme. Also, we introduce a deep-learned residual coding module that can further exploit spatial redundancy within a residual frame. Experiments conducted on SemanticKITTI and KITTI-360 datasets show that BIRD-PCC outperforms other methods in most bitrate conditions and generalizes well to unseen environments.

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Section: Inter-frame Predictionmentioning

confidence: 99%